# 作者 :南雨
# 时间 : 2022/6/28 9:12
class Trec_constant:
    batch_size = 64  # batch_size

    train_path = "../data/trec/train.txt"
    test_path = "../data/trec/test.txt"
    baidu_stop_words = "../data/baidu_stopwords.txt"

    w2v_model_path = "../data/word2vecModel2"
    cnn_lstm = "../data/CNN_LSTM"

    # word2vec
    vector_size = 300  # 每个词转化为向量时的维度,即1个词由一个100维的向量表示
    min_word_count = 1  # 保留的词的最低频度
    nums_workers = 2  # 并行化计算的CPU核心数
    window_size = 5  # 窗口大小，便于预测中心词，寻找词语间的相似度
    max_len = 40  # 每个句子扩充到统一长度

    # Conv1D
    filter_num = 64  # 卷积核个数
    filter_size = 3  # 卷积核大小
    conv_strides = 1  # 卷积核移动步长

    # MaxPool1D
    pool_size = 2  # 池化层窗口大小
    pool_strides = 1  # 池化窗口移动步长

    # LSTM
    lstm_output_size = 160  # LSTM层输出尺寸

    epoch = 7

    labels = ["DESC", "ENTY", "ABBR", "HUM", "NUM", "LOC"]  # Trec一级标签
    sub_labels = [  # Trec二级标签
        "manner",
        "cremat",
        "animal",
        "exp",
        "ind",
        "gr",
        "title",
        "def",
        "date",
        "reason",
        "event",
        "state",
        "desc",
        "count",
        "other",
        "letter",
        "religion",
        "food",
        "country",
        "color",
        "termeq",
        "city",
        "body",
        "dismed",
        "mount",
        "money",
        "product",
        "period",
        "substance",
        "sport",
        "plant",
        "techmeth",
        "volsize",
        "instru",
        "abb",
        "speed",
        "word",
        "lang",
        "perc",
        "code",
        "dist",
        "temp",
        "symbol",
        "ord",
        "veh",
        "weight",
        "currency",
    ]
